Earnings Manipulation and Risky Investment

This paper develops a formal model to study earnings manipulation. It analyzes the effects of real earnings auditor quality and at-risk incentive on management's earnings manipulation decision. It shows that the management has the incentive to smooth corporate earnings even when the employment contract is linear. It also demonstrates that adding the ability to manipulate earnings to the principal-agent model drastically changes the management's attitude towards risk. The management will become risk seeking in the company's earnings when cumulative earnings management in previous periods is high even if the management has a risk-averse utility function

SpreadDetect: Detection of spreading change in a network over time

Change-point analysis has been successfully applied to the detect changes in multivariate data streams over time. In many applications, when data are observed over a graph/network, change does not occur simultaneously but instead spread from an initial source coordinate to the neighbouring coordinates over time. We propose a new method, SpreadDetect, that estimates both the source coordinate and the initial timepoint of change in such a setting. We prove that under appropriate conditions, the SpreadDetect algorithm consistently estimates both the source coordinate and the timepoint of change and that the minimal signal size detectable by the algorithm is minimax optimal. The practical utility of the algorithm is demonstrated through numerical experiments and a COVID-19 real dataset.Comment: 26 pages,3 figures, 2 table

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.Comment: Accepted by ICCV 201